
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import cvxopt as opt
from cvxopt import blas, solvers
import pandas as pd

np.random.seed(123)

# Turn off progress printing 
solvers.options['show_progress'] = False


import plotly
import cufflinks


# (*) To communicate with Plotly's server, sign in with credentials file
import chart_studio.plotly as py  

# (*) Useful Python/Plotly tools
import plotly.tools as tls   

# (*) Graph objects to piece together plots
from plotly.graph_objs import *


stocks_df = pd.read_csv('stock.csv').drop(columns = "Unnamed: 0")
stocks_df


np.random.seed(1245231)

# Create random weights for the stocks and normalize them
weights = np.array(np.random.random(10))

# Ensure that the sum of all weights are = 1
weights = weights / np.sum(weights) 
print(weights)

[0.10238429 0.16104134 0.07857652 0.14602368 0.1111931  0.14945584
 0.02853777 0.07222408 0.10414996 0.04641342]


from usfl_func import *


df_portfolio = portfolio_allocation(df = stocks_df, weights = weights)
df_portfolio


metrics = portfolio_statistical_analysis(stocks_df, weights)
print("  - Portfolio Cummulative Return = {:.02f}%".format(metrics[0]))
print('  - Portfolio Expected return per year = {:.02f}%'.format(metrics[1] * 100))
print('  - Volatility (standard deviation) of the portfolio = {:.02f}%'.format(metrics[2] * 100))
print('  - Sharpe ratio of the portfolio = {}'.format(metrics[3]))

  - Portfolio Cummulative Return = 274.64%
  - Portfolio Expected return per year = 27.92%
  - Volatility (standard deviation) of the portfolio = 24.76%
  - Sharpe ratio of the portfolio = 1.127892923864166


# Create random weights and analayze the portfolio to find the optimal weight allocation

number_of_trials = 2000

# Placeholder to store the weights
possible_weights_runs = np.zeros((number_of_trials, 10))

# Placeholder to store the sharpe ratios
sharpe_ratio_runs = np.zeros(number_of_trials)

# Placeholder to store the returns
expected_portfolio_returns_runs = np.zeros(number_of_trials)

# Placeholder to store the volatility
volatility_runs = np.zeros(number_of_trials)

# Placeholder to store the cummulative returns
cummulative_returns_runs = np.zeros(number_of_trials)

np.random.seed(0)

import time

for i in range(number_of_trials):
    # Random Weights
    t = 1000 * time.time()
    np.random.seed(int(t) % 2**32)
    weights = np.array(np.random.random(10))
    weights = weights / np.sum(weights)
    
    # Store the weights
    possible_weights_runs[i,:] = weights
    
    # Store the sharpe ratio, return and volatility
    cummulative_returns_runs[i], expected_portfolio_returns_runs[i], volatility_runs[i], sharpe_ratio_runs[i]  = portfolio_statistical_analysis(stocks_df, weights)


sharpe_ratio_runs.max()

1.2449486308697835


possible_weights_runs[sharpe_ratio_runs.argmax(),:]

array([0.18464303, 0.14375508, 0.04119454, 0.1889962 , 0.01743844,
       0.20889257, 0.06154497, 0.02191524, 0.00496874, 0.1266512 ])


metrics = portfolio_statistical_analysis(stocks_df, possible_weights_runs[sharpe_ratio_runs.argmax(), :])
print("  - Portfolio Cummulative Return = {:.02f}%".format(metrics[0]))
print('  - Portfolio Expected return per year = {:.02f}%'.format(metrics[1] * 100))
print('  - Volatility (standard deviation) of the portfolio = {:.02f}%'.format(metrics[2] * 100))
print('  - Sharpe ratio of the portfolio = {}'.format(metrics[3]))

  - Portfolio Cummulative Return = 416.90%
  - Portfolio Expected return per year = 37.03%
  - Volatility (standard deviation) of the portfolio = 29.74%
  - Sharpe ratio of the portfolio = 1.2449486308697835


# Create a DataFrame with all volatilties, portfolio returns, and sharpe ratios
df_optimize = pd.DataFrame({'Volatility': volatility_runs.tolist(), 'Portfolio_Return': expected_portfolio_returns_runs.tolist(), 'Sharpe_Ratio': sharpe_ratio_runs.tolist() })


# Plot all the obtained volatility vs. returns 
# Highlight the volatility and return corresponding to highest sharpe ratio
fig = px.scatter(df_optimize, x = 'Volatility', y = 'Portfolio_Return', color = 'Sharpe_Ratio', size = 'Sharpe_Ratio', hover_data = ['Sharpe_Ratio'] )

# Let's add this line to highlight the point with the highest Sharpe Ratio
fig.add_trace(go.Scatter(x = [metrics[2]], y = [metrics[1]], mode = 'markers', name = 'Optimal Point', marker = dict(size=[50], color = 'black')))

fig.update_layout(coloraxis_colorbar = dict(
    title = "Sharpe Ratio",
    thicknessmode = "pixels", thickness = 25,
    lenmode = "pixels",
    yanchor = "middle", y = 0.4,
    dtick = 5
))

fig.show()


from scipy.optimize import minimize

# Since optimization works as a minimizing function, we take negative of sharpe ratio and then try minimize it.

# Define a function that calculates the negative Sharpe ratio
def calculate_negative_sharpe_func(weights, df = stocks_df):
    cum_return, exp_portfolio_return, volatility, sharpe_ratio = portfolio_statistical_analysis(stocks_df, weights)
    return sharpe_ratio * -1

# Function to define optimization constraints (make sure sum of all weights add to 1)
def optimization_constraints_func(weights, df = stocks_df,):
    return np.sum(weights) - 1

# Function to obtain the "volatility" for a given set of portfolio weights
def calculate_volatility_func(weights, df = stocks_df):
    cum_return, exp_portfolio_return, volatility, sharpe_ratio = portfolio_statistical_analysis(stocks_df, weights)
    return volatility

# Function to get the return for a particular weight
def calculate_return_func(weights, df = stocks_df):
    cum_return, exp_portfolio_return, volatility, sharpe_ratio = portfolio_statistical_analysis(stocks_df, weights)
    return exp_portfolio_return


# Constraints to pass to the optimizer
optimization_constraint = ({'type':'eq','fun': optimization_constraints_func})

# Lower and upper bounds for weights
bounds = ((0, 1), (0, 1), (0, 1), (0, 1),(0, 1), (0, 1), (0, 1), (0, 1),(0,1), (0, 1))

# Initial weight assumption
t = 1000 * time.time()
np.random.seed(int(t) % 2**32)
weights = np.array(np.random.random(10))
initialization = weights / np.sum(weights)


sum(initialization)

1.0


# Optimization Function
# Optimization Algorithms is SLSQP stands for Sequential Least Squares Programming (SLSQP)   
optimization_results = minimize(calculate_negative_sharpe_func, initialization, method = 'SLSQP', bounds = bounds, constraints = optimization_constraint)


optimized_weights = optimization_results.x
optimized_weights

array([2.19062537e-01, 1.43329371e-01, 5.39390581e-17, 4.45791641e-01,
       3.69848466e-17, 1.91816451e-01, 4.64038530e-17, 3.49113100e-17,
       3.02051949e-18, 1.15603057e-17])


# Analyze the portfolio based on the obtained weights
cummulative_return_SLSQP, portfolio_return_value_SLSQP, vol_value_SLSQP, sharpe_ratio_SLSQP = portfolio_statistical_analysis(stocks_df, optimized_weights)


print('Best Portfolio metrics based on SLSQP optimizer:')
print('  - Optimal Cummulative return of the portfolio = {}%'.format(cummulative_return_SLSQP))
print('  - Portfolio Expected return per year = {:.02f}%'.format(portfolio_return_value_SLSQP * 100))
print('  - Volatility of the portfolio = {:.02f}%'.format(vol_value_SLSQP * 100))
print('  - Sharpe ratio of the portfolio = {}'.format(sharpe_ratio_SLSQP))

Best Portfolio metrics based on SLSQP optimizer:
  - Optimal Cummulative return of the portfolio = 462.87840798510445%
  - Portfolio Expected return per year = 38.20%
  - Volatility of the portfolio = 28.82%
  - Sharpe ratio of the portfolio = 1.3255064869799182

	Date	TSLA	WL-MART	AMZN	MSFOT	VISA	AAPL	GOOGL	NTFLX	JP-MORGN	NVDA
0	07-08-2017	71.470001	80.570000	49.532501	72.800003	100.919998	39.264999	46.452999	181.000000	93.889999	42.097500
1	08-08-2017	71.505997	81.169998	49.717499	72.089996	101.500000	39.650002	46.354500	181.369995	93.949997	43.472500
2	09-08-2017	72.199997	81.110001	49.130001	72.250000	100.760002	39.814999	46.030499	171.429993	92.980003	42.107498
3	10-08-2017	72.320000	81.070000	48.814999	71.900002	100.540001	39.974998	45.877499	174.029999	92.900002	43.040001
4	11-08-2017	71.393997	80.730003	48.000000	71.610001	99.550003	39.150002	45.398499	169.860001	92.129997	39.285000
...	...	...	...	...	...	...	...	...	...	...	...
1253	29-07-2022	842.099976	128.320007	134.899994	277.700012	212.000000	161.240005	113.400002	223.289993	115.589996	178.130005
1254	01-08-2022	903.830017	131.059998	134.960007	277.820007	208.449997	161.009995	115.529999	223.100006	114.500000	181.820007
1255	02-08-2022	882.010010	133.149994	134.720001	276.000000	207.800003	160.100006	114.430000	222.759995	113.919998	181.220001
1256	03-08-2022	915.000000	132.160004	136.210007	276.760010	207.990005	160.839996	116.339996	224.789993	113.449997	181.839996
1257	04-08-2022	933.000000	130.669998	140.580002	281.799988	210.529999	166.009995	118.300003	227.679993	112.849998	188.490005

	Date	TSLA	WL-MART	AMZN	MSFOT	VISA	AAPL	GOOGL	NTFLX	JP-MORGN	NVDA	portfolio daily worth in $	portfolio daily % return
0	07-08-2017	5119.214677	8052.066909	3928.825849	7301.184136	5559.655017	7472.791869	1426.888335	3611.204103	5207.498148	2320.670958	50000.000000	0.000000
1	08-08-2017	5121.792979	8112.029972	3943.499546	7229.976833	5591.607168	7546.064437	1423.862759	3618.586023	5210.825866	2396.469344	50194.714927	0.389430
2	09-08-2017	5171.502437	8106.033946	3896.900298	7246.023792	5550.840881	7577.466150	1413.910479	3420.269029	5157.026292	2321.222109	49861.195411	-0.664451
3	10-08-2017	5180.097947	8102.036295	3871.914925	7210.922147	5538.721086	7607.916660	1409.210806	3472.142798	5152.589131	2372.627361	49918.179156	0.114285
4	11-08-2017	5113.770703	8068.057412	3807.270720	7181.837661	5484.182368	7450.906000	1394.497450	3388.945484	5109.881711	2165.628804	49164.978313	-1.508871
...	...	...	...	...	...	...	...	...	...	...	...	...	...
1253	29-07-2022	60317.482808	12824.144000	10700.016611	27850.808221	11679.021868	30686.693724	3483.287268	4454.948834	6411.062909	9819.612312	178227.078555	1.713870
1254	01-08-2022	64739.048884	13097.975337	10704.776730	27862.842638	11483.453176	30642.918940	3548.713999	4451.158327	6350.607565	10023.027728	182904.523325	2.624430
1255	02-08-2022	63176.137194	13306.846972	10685.739901	27680.312340	11447.645185	30469.732678	3514.925530	4444.374630	6318.438438	9989.951738	181034.104605	-1.022620
1256	03-08-2022	65539.126401	13207.908587	10803.924405	27756.534493	11458.112343	30610.565262	3573.594530	4484.875940	6292.370386	10024.129644	183751.141991	1.500843
1257	04-08-2022	66828.420691	13058.999216	11150.544280	28261.998860	11598.039916	31594.503310	3633.799709	4542.535497	6259.092149	10390.718699	187318.652327	1.941490

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